Increased likelihood of heat-induced large wildfires in the Mediterranean Basin

Responses in reef-building corals to wildfire emissions: Heterotrophic plasticity and calcification

Extreme wildfire events are on the rise globally, and although substantial wildfire emissions may find their way into the ocean, their impact on coral reefs remains uncertain. In a five-week laboratory experiment, we observed a significant reduction in photosynthesis in coral symbionts (Porites lutea) when exposed to fine particulate matter (PM2.5) from wildfires. At low PM2.5 level (2 mg L-1), the changes in δ13C and δ15N values in the host and symbiotic algae suggest reduced autotrophy and the utilization of wildfire particulates as a source of heterotrophic nutrients. This adaptive strategy, characterized by an increase in heterotrophy, sustained some aspects of coral growth (total biomass, proteins and lipids) under wildfire stress. Nevertheless, at high PM2.5 level (5 mg L-1), both autotrophy and heterotrophy significantly decreased, resulting in an imbalanced coral-algal nutritional relationship. These changes were related to light attenuation in seawater and particulate accumulation on the coral surface during PM2.5 deposition, ultimately rendering the coral growth unsustainable. Further, the calcification rates decreased by 1.5 to 1.85 times under both low and high levels of PM2.5, primarily affected by photosynthetic autotrophy rather than heterotrophy. Our study highlights a constrained heterotrophic plasticity of corals under wildfire stress. This limitation may restrict wildfire emissions as an alternative nutrient source to support coral growth and calcification, especially when oceanic food availability or autotrophy declines, as seen during bleaching induced by the warming ocean.

Spatio-temporal feature attribution of European summer wildfires with Explainable Artificial Intelligence (XAI)

Wildfires are among the most destructive natural disasters globally. Understanding the drivers behind wildfires is a crucial aspect of preventing and managing them. Machine learning methods have gained popularity in wildfire modeling in recent years, but their algorithms are usually complex and challenging to interpret. In this study, we employed the SHapley Additive exPlanations (SHAP) value, an Explainable Artificial Intelligence method, to interpret the model and thus generate spatio-temporal feature attributions. Our research focuses on the forest, shrub and herbaceous vegetated areas of Europe during the summers from 2018 to 2022. Using burned areas, meteorology, vegetation, topography, and anthropogenic activity data, we established a wildfire occurrence model using random forest classification. The model was highly accurate, with an Area Under the Receiver Operating Characteristic Curve of 0.940. The SHAP results revealed six features that significantly influence wildfire occurrences: land surface temperature (LST), solar radiation (SR), Temperature Condition Index (TCI), Normalized Difference Vegetation Index (NDVI), precipitation (Prep), and soil moisture (SM). The tipping points for the positive or negative shifts in contributions are around 30 °C (LST), 2.20e7 J/m^2 (SR), 0.2 (TCI), 0.78 (NDVI), 2 mm/h (Prep), and 0.18 (SM). These predictors display strong spatial variability in their contribution levels. In Southern Europe, LST and SR emerge as the primary contributors to wildfires, making substantial impacts. Conversely, in regions at mid and high latitudes in Europe, NDVI, Prep, and SM assume a more prominent role in promoting wildfires, albeit with relatively smaller contributions. Furthermore, the disparities in SHAP values for TCI and SMCI across different years provide valuable insights into the effects of variation in regional meteorological conditions on wildfires. Our study provides a new approach to uncovering the spatio-temporal variations of feature contributions, which will help to better understand the mechanism of wildfire occurrence and enhance prevention and mitigation.

Coupling heat wave and wildfire occurrence across multiple ecoregions within a Eurasia longitudinal gradient

Understanding the relationship between heat wave occurrence and wildfire spread represents a key priority in global change studies due to the significant threats posed on natural ecosystems and society. Previous studies have not explored the spatial and temporal mechanism underlying the relationship between heat waves and wildfires occurrence, especially over large geographical regions. This study seeks to investigate such a relationship with a focus on 37 ecoregions within a Eurasia longitudinal gradient. The analysis is based on the wildfire dataset provided by the GlobFire Final Fire Event Detection and the meteorological dataset ERA5-land from Copernicus Climate service. In both cases we focused on the 2001-2019 timeframe. By means of a 12 km square grid, three wildfire metrics, i.e., density, seasonality, and severity of wildfires, were computed as proxy of fire regime. Heat waves were also characterized in terms of periods, duration, and intensity for the same period. Statistical tests were performed to evaluate the different patterns of heat wave and wildfire occurrence in the 37 ecoregions within the study area. By using Geographically Weighted Regression (GWR) we modeled the spatial varying relationships between heat wave characteristics and wildfire metrics. As expected, our results suggest that the 37 ecoregions identified within the Eurasia longitudinal gradient differ in terms of fire regimes. However, the occurrence of heat waves did not show significant differences among ecoregions, but a more evident variability in terms of relationship between fire regime metrics and heat waves within the study area. The outcome of the GWR analysis allowed us to identify the spatial locations (i.e., hotspot areas) where the relationship between heat waves and wildfires is positive and significant. Hence, in hotspots the presence of heat waves can be seen as a driver of wildfire occurrence in forest and steppe ecosystems. The findings from this study could contribute to a more comprehensive assessment of wildfire patterns in this geographical region, thus supporting cross-regional prevention strategies for disaster risk mitigation.

Plant hydraulics at the heart of plant, crops and ecosystem functions in the face of climate change

Plant hydraulics is crucial for assessing the plants' capacity to extract and transport water from the soil up to their aerial organs. Along with their capacity to exchange water between plant compartments and regulate evaporation, hydraulic properties determine plant water relations, water status and susceptibility to pathogen attacks. Consequently, any variation in the hydraulic characteristics of plants is likely to significantly impact various mechanisms and processes related to plant growth, survival and production, as well as the risk of biotic attacks and forest fire behaviour. However, the integration of hydraulic traits into disciplines such as plant pathology, entomology, fire ecology or agriculture can be significantly improved. This review examines how plant hydraulics can provide new insights into our understanding of these processes, including modelling processes of vegetation dynamics, illuminating numerous perspectives for assessing the consequences of climate change on forest and agronomic systems, and addressing unanswered questions across multiple areas of knowledge.

Twenty-first-century demographic and social inequalities of heat-related deaths in Brazilian urban areas

Population exposure to heat waves (HWs) is increasing worldwide due to climate change, significantly affecting society, including public health. Despite its significant vulnerabilities and limited adaptation resources to rising temperatures, South America, particularly Brazil, lacks research on the health impacts of temperature extremes, especially on the role played by socioeconomic factors in the risk of heat-related illness. Here, we present a comprehensive analysis of the effects of HWs on mortality rates in the 14 most populous urban areas, comprising approximately 35% of the country's population. Excess mortality during HWs was estimated through the observed-to-expected ratio (O/E) for total deaths during the events identified. Moreover, the interplay of intersectionality and vulnerability to heat considering demographics and socioeconomic heterogeneities, using gender, age, race, and educational level as proxies, as well as the leading causes of heat-related excess death, were assessed. A significant increase in the frequency was observed from the 1970s (0-3 HWs year-1) to the 2010s (3-11 HWs year-1), with higher tendencies in the northern, northeastern, and central-western regions. Over the 2000-2018 period, 48,075 (40,448-55,279) excessive deaths were attributed to the growing number of HWs (>20 times the number of landslides-related deaths for the same period). Nevertheless, our event-based surveillance analysis did not detect the HW-mortality nexus, reinforcing that extreme heat events are a neglected disaster in Brazil. Among the leading causes of death, diseases of the circulatory and respiratory systems and neoplasms were the most frequent. Critical regional differences were observed, which can be linked to the sharp North-South inequalities in terms of socioeconomic and health indicators, such as life expectancy. Higher heat-related excess mortality was observed for low-educational level people, blacks and browns, older adults, and females. Such findings highlight that the strengthening of primary health care combined with reducing socioeconomic, racial, and gender inequalities represents a crucial step to reducing heat-related deaths.

Wildfire precursors show complementary predictability in different timescales

In most of the world, conditions conducive to wildfires are becoming more prevalent. Net carbon emissions from wildfires contribute to a positive climate feedback that needs to be monitored, quantified, and predicted. Here we use a causal inference approach to evaluate the influence of top-down weather and bottom-up fuel precursors on wildfires. The top-down dominance on wildfires is more widespread than bottom-up dominance, accounting for 73.3% and 26.7% of regions, respectively. The top-down precursors dominate in the tropical rainforests, mid-latitudes, and eastern Siberian boreal forests. The bottom-up precursors dominate in North American and European boreal forests, and African and Australian savannahs. Our study identifies areas where wildfires are governed by fuel conditions and hence where fuel management practices may be more effective. Moreover, our study also highlights that top-down and bottom-up precursors show complementary wildfire predictability across timescales. Seasonal or interannual predictions are feasible in regions where bottom-up precursors dominate.

Functional response and resistance to drought in seedlings of six shrub species with contrasting leaf traits from the Mediterranean Basin and California

Extreme drought events during post-fire regeneration are becoming increasingly frequent in Mediterranean-type ecosystems. Understanding how plants with different traits and origins respond to such conditions during early life stages is therefore critical for assessing the effect of climate change. Here, seedlings of three Cistus (semi-deciduous malacophylls from the Mediterranean Basin) and three Ceanothus (evergreen sclerophylls from California) species, two post-fire seeder genera with contrasting leaf traits, were subjected to complete water deprivation for 3 months in a common garden experiment. The leaf and plant structure and plant tissue water relations were characterized before the drought, and the functional responses (water availability, gas exchange and fluorescence) were monitored during the drought. Both genera exhibited contrasting leaf structure and tissue water relations traits, with higher leaf area and specific leaf area as well as higher osmotic potential at maximum turgor and turgor loss point in Cistus than Ceanothus. During drought, Ceanothus showed a more conservative use of water than Cistus, with a water potential less sensitive to decreasing soil moisture and a strong decline in photosynthesis and stomatal conductance in response to water deficit, but also a level of fluorescence more responsive to drought than Cistus. However, we could not find a different degree of drought resistance between the genera. This was particularly clear between Cistus ladanifer L. and Ceanothus pauciflorus DC., the two most functionally contrasting species, but at the same time, the two most drought-resistant. Our findings demonstrate that species with different leaf traits and functional responses to water stress may not differ in their degree of drought resistance, at least during the seedling stage. This underlines the need to take general categorizations by genus or functional types with caution and to deepen our knowledge about the Mediterranean-type species ecophysiology, especially during early life stages, in order to anticipate their vulnerability to climate change.

Fire-Pollutant-Atmosphere Components and Its Impact on Mortality in Portugal During Wildfire Seasons

This study analyzed fire-pollutant-meteorological variables and their impact on cardio-respiratory mortality in Portugal during wildfire season. Data of burned area, particulate matter with a diameter of 10 or 2.5 μm (μm) or less (PM10, PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), temperature, relative humidity, wind speed, aerosol optical depth and mortality rates of Circulatory System Disease (CSD), Respiratory System Disease (RSD), Pneumonia (PNEU), Chronic Obstructive Pulmonary Disease, and Asthma (ASMA), were used. Only the months of 2011-2020 wildfire season (June-July-August-September-October) with a burned area greater than 1,000 ha were considered. Principal component analysis was used on fire-pollutant-meteorological variables to create two indices called Pollutant-Burning Interaction (PBI) and Atmospheric-Pollutant Interaction (API). PBI was strongly correlated with the air pollutants and burned area while API was strongly correlated with temperature and relative humidity, and O3. Cluster analysis applied to PBI-API divided the data into two Clusters. Cluster 1 included colder and wetter months and higher NO2 concentration. Cluster 2 included warmer and dried months, and higher PM10, PM2.5, CO, and O3 concentrations. The clusters were subjected to Principal Component Linear Regression to better understand the relationship between mortality and PBI-API indices. Cluster 1 showed statistically significant (p-value < 0.05) correlation (r) between RSDxPBI (r RSD = 0.58) and PNEUxPBI (r PNEU = 0.67). Cluster 2 showed statistically significant correlations between RSDxPBI (r RSD = 0.48), PNEUxPBI (r PNEU = 0.47), COPDxPBI (r COPD = 0.45), CSDxAPI (r CSD = 0.70), RSDxAPI (r CSD = 0.71), PNEUxAPI (r PNEU = 0.49), and COPDxAPI (r PNEU = 0.62). Cluster 2 analysis indicates that the warmest, driest, and most polluted months of the wildfire season were associated with cardio-respiratory mortality.

Climate change, large fires, and cultural landscapes in the mediterranean basin: An analysis in southern Spain

Understanding the factors that influence fire regimes in Mediterranean climates is essential to reduce their risk. This research uses Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite resources to evaluate recent changes in land surface temperature, precipitation, and vegetation and their effects in the occurrence of large fires in the Mediterranean Basin. The results of the analysis of 335 fire events occurred in southern Spain from 2001 to 2020 show an increase in hazardous meteorological factors linked to droughts and thermal anomalies. The study also examines the potential of preserving traditional landscapes to minimize such risk. In fact, the maintenance and recovering of traditional agro-pastoral activities is an effective option to reduce flammability and increase the resilience of cultural landscapes in hazardous climatic conditions.

Drivers and implications of the extreme 2022 wildfire season in Southwest Europe

Wildfire is a common phenomenon in Mediterranean countries but the 2022 fire season has been extreme in southwest Europe (Portugal, Spain and France). Here we provide a preliminary but comprehensive analysis of 2022's wildfire season in southwest Europe. Burned area has exceeded the 2001-2021 median by a factor of 52 in some regions and large wildfires (>500 ha) started to occur in June-July, earlier than the traditional fire season. These anomalies were associated with record-breaking values of fuel dryness, atmospheric water demand and pyrometeorological conditions. Live fuel moisture content was below the historical minima for almost 50 % of the season in some regions. A few large wildfires were responsible for 82 % of the burned area and, in turn, 47 % of the area burned occurred in protected areas. Shrublands, transitional woodlands and conifer forests (but not eucalypt plantations) were the land cover types most affected by extreme fires. As climate change intensifies, we can expect such fire seasons to become the new normal in large parts of the continent, potentially leading to major negative impacts on rural economies. These results highlight the need for landscape level fuel management also in protected areas, to avoid fire-induced biodiversity losses and landscape scale degradation. Our results have important policy implications and indicate that fire prevention should be explicitly addressed within continental forest legislation and strategies.

Plant hydraulic modelling of leaf and canopy fuel moisture content reveals increasing vulnerability of a Mediterranean forest to wildfires under extreme drought

Fuel moisture content (FMC) is a crucial driver of forest fires in many regions world-wide. Yet, the dynamics of FMC in forest canopies as well as their physiological and environmental determinants remain poorly understood, especially under extreme drought. We embedded a FMC module in the trait-based, plant-hydraulic SurEau-Ecos model to provide innovative process-based predictions of leaf live fuel moisture content (LFMC) and canopy fuel moisture content (CFMC) based on leaf water potential ( ψ Leaf ). SurEau-Ecos-FMC relies on pressure-volume (p-v) curves to simulate LFMC and vulnerability curves to cavitation to simulate foliage mortality. SurEau-Ecos-FMC accurately reproduced ψ Leaf and LFMC dynamics as well as the occurrence of foliage mortality in a Mediterranean Quercus ilex forest. Several traits related to water use (leaf area index, available soil water, and transpiration regulation), vulnerability to cavitation, and p-v curves (full turgor osmotic potential) had the greatest influence on LFMC and CFMC dynamics. As the climate gets drier, our results showed that drought-induced foliage mortality is expected to increase, thereby significantly decreasing CFMC. Our results represent an important advance in our capacity to understand and predict the sensitivity of forests to wildfires.

Heatwaves and fire in Pantanal: Historical and future perspectives from CORDEX-CORE

The Pantanal biome, at the confluence of Brazil, Bolivia and Paraguay, is the largest continental wetland on the planet and an invaluable reserve of biodiversity. The exceptional 2020 fire season in Pantanal drew particular attention due to the severe wildfires and the catastrophic natural and socio-economic impacts witnessed within the biome. So far, little progress has been made in order to better understand the influence of climate extremes on fire occurrence in Pantanal. Here, we evaluate how extreme hot conditions, through heatwave events, are related to the occurrence and the exacerbation of fires in this region. A historical analysis using a statistical regression model found that heatwaves during the dry season explained 82% of the interannual variability of burned area during the fire season. In a future perspective, an ensemble of CORDEX-CORE simulations assuming different Representative Concentration Pathways (RCP2.6 and RCP8.5), reveal a significant increasing trend in heatwave occurrence over Pantanal. Compared to historical levels, the RCP2.6 scenario leads to more than a doubling in the Pantanal heatwave incidence during the dry season by the second half of the 21st century, followed by a plateauing. Alternatively, RCP8.5 projects a steady increase of heatwave incidence until the end of the century, pointing to a very severe scenario in which heatwave conditions would be observed nearly over all the Pantanal area and during practically all the days of the dry season. Accordingly, favorable conditions for fire spread and consequent large burned areas are expected to occur more often in the future, posing a dramatic short-term threat to the ecosystem if no preservation action is undertaken.

The Effects of Coexposure to Extremes of Heat and Particulate Air Pollution on Mortality in California: Implications for Climate Change

Rationale: Extremes of heat and particulate air pollution threaten human health and are becoming more frequent because of climate change. Understanding the health impacts of coexposure to extreme heat and air pollution is urgent. Objectives: To estimate the association of acute coexposure to extreme heat and ambient fine particulate matter (PM2.5) with all-cause, cardiovascular, and respiratory mortality in California from 2014 to 2019. Methods: We used a case-crossover study design with time-stratified matching using conditional logistic regression to estimate mortality associations with acute coexposures to extreme heat and PM2.5. For each case day (date of death) and its control days, daily average PM2.5 and maximum and minimum temperatures were assigned (0- to 3-day lag) on the basis of the decedent's residence census tract. Measurements and Main Results: All-cause mortality risk increased 6.1% (95% confidence interval [CI], 4.1-8.1) on extreme maximum temperature-only days and 5.0% (95% CI, 3.0-8.0) on extreme PM2.5-only days, compared with nonextreme days. Risk increased by 21.0% (95% CI, 6.6-37.3) on days with exposure to both extreme maximum temperature and PM2.5. Increased risk of cardiovascular and respiratory mortality on extreme coexposure days was 29.9% (95% CI, 3.3-63.3) and 38.0% (95% CI, -12.5 to 117.7), respectively, and were more than the sum of individual effects of extreme temperature and PM2.5 only. A similar pattern was observed for coexposure to extreme PM2.5 and minimum temperature. Effect estimates were larger over age 75 years. Conclusions: Short-term exposure to extreme heat and air pollution alone were individually associated with increased risk of mortality, but their coexposure had larger effects beyond the sum of their individual effects.

Model-assisted ideotyping reveals trait syndromes to adapt viticulture to a drier climate

Climate change is challenging the resilience of grapevine (Vitis), one of the most important crops worldwide. Adapting viticulture to a hotter and drier future will require a multifaceted approach including the breeding of more drought-tolerant genotypes. In this study, we focused on plant hydraulics as a multi-trait system that allows the plant to maintain hydraulic integrity and gas exchange rates longer under drought. We quantified a broad range of drought-related traits within and across Vitis species, created in silico libraries of trait combinations, and then identified drought tolerant trait syndromes. By modeling the maintenance of hydraulic integrity of current cultivars and the drought tolerant trait syndromes, we identified elite ideotypes that increased the amount of time they could experience drought without leaf hydraulic failure. Generally, elites exhibited a trait syndrome with lower stomatal conductance, earlier stomatal closure, and a larger hydraulic safety margin. We demonstrated that, when compared with current cultivars, elite ideotypes have the potential to decrease the risk of hydraulic failure across wine regions under future climate scenarios. This study reveals the syndrome of traits that can be leveraged to protect grapevine from experiencing hydraulic failure under drought and increase drought tolerance.

Estimation of mercury emissions from the forest floor of a pine plantation during a wildfire in central Portugal

Mercury (Hg) concentrations in soils and Hg releases from soils during wildfires are not well characterised in Portugal, even though wildfire activity continues to increase around the Mediterranean. This study focused on the low to moderate severity wildfire in Pombal (Portugal) in 2019, which consumed 12.5 ha of maritime pine (Pinus pinaster Ait.). We evaluated Hg concentrations in soil profiles and Hg pools in organic horizons to assess the fire-induced Hg emissions. Moreover, impacts of the fire on forest floor properties were estimated. Four soil profiles were sampled, two at the burned area and two at a nearby unburned area. The soil profiles displayed a typical Hg distribution, with higher Hg concentrations (156 µg kg^-1) in the organic horizons with a sharp decrease in the mineral layers. The bond between organic matter and Hg was evident along the profiles, with a strong correlation between TOC and Hg. Ratios of Hg/TOC in the surface layers of the soil were similar in all profiles. The mean organic Hg pool at the studied site was calculated at 10.6 g ha^-1. The fire did not seem to affect the topsoil properties based on visual indicators and the lack of statistical differences (p > 0.05) among measured fire-sensitive chemical soil properties (pH, CEC, TOC, TS) between the topsoils of the burned and unburned areas. If we consider a hypothetical complete combustion of the organic layer (743 Mg) and unaffected topsoil, we estimated a release of 133 g of Hg from the burned area. The study emphasised the importance of the forest floor for Hg retention and its crucial role in Hg emissions during wildfires in a country increasingly affected by climate change.

Drought-heatwave nexus in Brazil and related impacts on health and fires: A comprehensive review

Climate change is drastically altering the frequency, duration, and severity of compound drought-heatwave (CDHW) episodes, which present a new challenge in environmental and socioeconomic sectors. These threats are of particular importance in low-income regions with growing populations, fragile infrastructure, and threatened ecosystems. This review synthesizes emerging progress in the understanding of CDHW patterns in Brazil while providing insights about the impacts on fire occurrence and public health. Evidence is mounting that heatwaves are becoming increasingly linked with droughts in northeastern and southeastern Brazil, the Amazonia, and the Pantanal. In those regions, recent studies have begun to build a better understanding of the physical mechanisms behind CDHW events, such as the soil moisture-atmosphere coupling, promoted by exceptional atmospheric blocking conditions. Results hint at a synergy between CDHW events and high fire activity in the country over the last decades, with the most recent example being the catastrophic 2020 fires in the Pantanal. Moreover, we show that HWs were responsible for increasing mortality and preterm births during record-breaking droughts in southeastern Brazil. This work paves the way for a more in-depth understanding on CDHW events and their impacts, which is crucial to enhance the adaptive capacity of different Brazilian sectors.

Mediterranean moth diversity is sensitive to increasing temperatures and drought under climate change

Climate change affects ecosystems worldwide and is threatening biodiversity. Insects, as ectotherm organisms, are strongly dependent on the thermal environment. Yet, little is known about the effects of summer heat and drought on insect diversity. In the Mediterranean climate zone, a region strongly affected by climate change, hot summers might have severe effects on insect communities. Especially the larval stage might be sensitive to thermal variation, as larvae—compared to other life stages—cannot avoid hot temperatures and drought by dormancy. Here we ask, whether inter-annual fluctuations in Mediterranean moth diversity can be explained by temperature (T_Larv) and precipitation during larval development (H_Larv). To address our question, we analyzed moth communities of a Mediterranean coastal forest during the last 20 years. For species with summer-developing larvae, species richness was significantly negatively correlated with T_Larv, while the community composition was affected by both, T_Larv and H_Larv. Therefore, summer-developing larvae seem particularly sensitive to climate change, as hot summers might exceed the larval temperature optima and drought reduces food plant quality. Increasing frequency and severity of temperature and drought extremes due to climate change, therefore, might amplify insect decline in the future.

Global warming is shifting the relationships between fire weather and realized fire-induced CO2 emissions in Europe

Fire activity has significantly changed in Europe over the last decades (1980–2020s), with the emergence of summers attaining unprecedented fire prone weather conditions. Here we report a significant shift in the non-stationary relationship linking fire weather conditions and fire intensity measured in terms of CO₂ emissions released during biomass burning across a latitudinal gradient of European IPCC regions. The reported trends indicate that global warming is possibly inducing an incipient change on regional fire dynamics towards increased fire impacts in Europe, suggesting that emerging risks posed by exceptional fire-weather danger conditions may progressively exceed current wildfire suppression capabilities in the next decades and impact forest carbon sinks.

Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices

Wildfire simulations depend on fuel representation. Present fuel models are mainly based on the density and properties of different vegetation types. This study aims to improve the accuracy of WRF-Fire wildfire simulations, by using synthetic-aperture radar (SAR) data to estimate the fuel load and the trend of vegetation index to estimate the dryness of woody vegetation. We updated the chaparral and timber standard woody fuel classes in the WRF-Fire fuel settings. We used the ESA global above-ground biomass (AGB) based on SAR data to estimate the fuel load, and the Landsat normalized difference vegetation index (NDVI) trends of woody vegetation to estimate the fuel moisture content. These fuel sub-parameters represent the dynamic changes and spatial variability of woody fuel. We simulated two wildfires in Israel while using three different fuel models: the original 13 Anderson Fire Behavior fuel model, and two modified fuel models introducing AGB alone, and AGB and dryness. The updated fuel model (the basic fuel model plus the AGB and dryness) improved the simulation results significantly, i.e., the Jaccard similarity coefficient increased by 283% on average. Our results demonstrate the potential of combining satellite SAR data and Landsat NDVI trends to improve WRF-Fire wildfire simulations.

Comparison of Wildfire Meteorology and Climate at the Adriatic Coast and Southeast Australia

Wildfire is one of the most complex natural hazards. Its origin is a combination of anthropogenic factors, urban development and weather plus climate factors. In particular, weather and climate factors possess many spatiotemporal scales and various degrees of predictability. Due to the complex synergy of the human and natural factors behind the events, every wildfire is unique. However, there are indeed common meteorological and climate factors leading to the high fire risk before certain ignition mechanismfigures occur. From a scientific point of view, a better understanding of the meteorological and climate drivers of wildfire in every region would enable more effective seasonal to annual outlook of fire risk, and in the long term, better applications of climate projections to estimate future scenarios of wildfire. This review has performed a comparison study of two fire-prone regions: southeast Australia including Tasmania, and the Adriatic coast in Europe, especially events in Croatia. The former is well known as part of the ‘fire continent’, and major resources have been put into wildfire research and forecasting. The Adriatic coast is a region where some of the highest surface wind speeds, under strong topographic effect, have been recorded and, over the years, have coincided with wildfire ignitions. Similar synoptic background and dynamic origins of the meso-micro-scale meteorological conditions of these high wind events as well as the accompanied dryness have been identified between some of the events in the two regions. We have also reviewed how the researchers from these two regions have applied different weather indices and numerical models. The status of estimating fire potential under climate change for both regions has been evaluated. This review aims to promote a global network of information exchange to study the changing anthropogenic and natural factors we have to confront in order to mitigate and adapt the impacts and consequences from wildfire.

Nitrogen Enrichment during Soil Organic Matter Burning and Molecular Evidence of Maillard Reactions

Wildfires in forested watersheds dramatically alter stored and labile soil organic matter (SOM) pools and the export of dissolved organic matter (DOM). Ecosystem recovery after wildfires depends on soil microbial communities and revegetation and therefore is limited by the availability of nutrients, such as nitrogen-containing and labile, water-soluble compounds. However, SOM byproducts produced at different wildfire intensities are poorly understood, leading to difficulties in assessing wildfire severity and predicting ecosystem recovery. In this work, water-extractable organic matter (WEOM) from laboratory microcosms of soil burned at discrete temperatures was characterized by ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry to study the impacts of fire temperature on SOM and DOM composition. The molecular composition derived from different burn temperatures indicated that nitrogen-containing byproducts were enriched with heating and composed of a wide range of aromatic features and oxidation states. Mass difference-based analysis also suggested that products formed during heating could be modeled using transformations along the Maillard reaction pathway. The enrichment of N-containing SOM and DOM at different soil burning intensities has important implications for ecosystem recovery and downstream water quality.

Analysis of Wildfires in the Mid and High Latitudes Using a Multi-Dataset Approach: A Case Study in California and Krasnoyarsk Krai

In this study, we investigate the emissions from wildfires in the mid latitude (California) and high latitude (Krasnoyarsk Krai) during the periods of 16–17 August 2020 and 28 July 2019, respectively. Wildfires are unique in themselves as they are driven by various factors such as fuel type, topology, and meteorology. In this study, we analyze whether there are any major variations in the emissions and transport of pollutants between two large wildfire cases in the mid latitude of California and high latitude of Krasnoyarsk Krai. The study is important to understand and characterize the emission regime from biomass burning of different land covers using a mutli-dataset approach. We analyze whether there are any major variations in the emissions and transport of pollutants from these wildfires. For example, the aerosol extinction coefficient profile showed smoke detected at the highest altitude of 9 km in Krasnoyarsk Krai, whereas in California the highest altitude was observed at approximately 6 km. Moreover, large values of black carbon (BC) concentration were observed in Krasnoyarsk Krai approximately 7 µg/m3 compared to the 0.44 µg/m3 observed in California. Areas with an immense dense vegetation are prone to large emissions. The results from this case study suggest that high latitude wildfires emit more pollutants than mid latitude wildfires. However, more studies in the future will be conducted to conclude this observation and finding with certainty.

Accurate tracking of forest activity key to multi-jurisdictional management goals: A case study in California

An essential component of sustainable forest management is accurate monitoring of forest activities. Although monitoring efforts have generally increased for many forests throughout the world, in practice, effective monitoring is complex. Determining the magnitude and location of progress towards sustainability targets can be challenging due to diverse forest operations across multiple jurisdictions, the lack of data standardization, and discrepancies between field inspections and remotely-sensed records. In this work, we used California as a multijurisdictional case study to explore these problems and develop an approach that broadly informs forest monitoring strategies. The State of California recently entered into a shared stewardship agreement with the US Forest Service (USFS) and set a goal to jointly treat one million acres of forest and rangeland annually by 2025. Currently, however, federal and state forest management datasets are disjoint. This work addresses three barriers stymying the use of federal and state archival records to assess management goals. These barriers are: 1) current databases from different jurisdictions have not been combined due to their distinct data collection processes and internal structures; 2) datasets have not been comprehensively analyzed, despite the need to understand the extent of previous treatments as well as the rate of current activity; and 3) the spatial accuracy of archival datasets has not been evaluated against remotely-sensed data. To reduce these barriers, we first aggregated existing archival forest management records between 1984 and 2019 from the USFS' Forest Activity Tracking System (FACTS) and the California Department of Forestry and Fire Protection (CAL FIRE) using a qualitative scalar of treatment intensity. Combined FACTS and CAL FIRE completed footprint acres - defined as unique areas of land where a treatment was completed at any time since 1984 - have decreased since a peak in 2008. At most, 300,000 footprint acres are completed each year, 30% of the million-acre goal. Prescribed fires - defined as direct burning operations - have risen over time, according to the FACTS hazardous fuels dataset but prescribed fire records in CAL FIRE's dataset have rapidly increased since 2016. We also refined the spatial and temporal detail of the aggregated management record using the Continuous Change Detection and Classification algorithm on satellite remote sensing data to produce a state-wide time series map of harvest disturbances. A comparison of the algorithm's refined data to the archival record potentially suggests over-reporting in both FACTS and CAL FIRE's archival datasets. Our integrated dataset provides a better assessment of current treatments and the path towards the 1-million-acre a year goal. The refined dataset leverages the strengths of complementary, albeit imperfect, monitoring strategies from archives and remotely-sensed detection.

Trends in Fire Danger and Population Exposure along the Wildland-Urban Interface

The increased risk of wildfires and associated smoke exposure in the United States is a growing public health problem, particularly along the Wildland-Urban Interface (WUI). Using the measure of fire danger, the Energy Release Component, we define fire danger as the onset and duration of fire season, in the continental US, between 1979 and 2016. We then combine the measure of fire danger with census data to quantify changes in population fire exposure across the WUI. We determined that the largest increases in fire danger were observed in the Southwest, Intermountain, and Pacific Southwest regions. The increased fire danger, specifically during peak fire season, accounted for 6.1 more fires each year and 78,000 more acres burned each year, underscoring the link between fire danger and the risks of large fire occurrence and burn acreage. Finally, we observed significant population growth (121.2% between 1990 and 2010) within high-danger WUI areas, further implying significant increases in potential fire exposure.

Species climatic niche explains post-fire regeneration of Aleppo pine (Pinus halepensis Mill.) under compounded effects of fire and drought in east Spain

Fire and drought are two major agents that shape Mediterranean ecosystems, but their interacting effects on forest resilience have not been yet fully addressed. We used Pinus halepensis to investigate how compound fire-drought regimes determine the success of post-fire regeneration. We measured the density of P.halepensis newly established individuals following fire in forty-three sites along the Spanish east coast, the wetter region of the species distribution. The climatic niche of P.halepensis was characterized by considering their populations across its Spanish distribution range. We used yearly values (1979-2013 period) of accumulated precipitation, mean temperature and the warmest quarter values of these two variables to generate the climatic space or climatic niche occupied by the species. Kernel density estimates were then applied to determine the niche centroid, which would correspond to the species' climatic optimum within its Spanish distribution range. Then, we computed the pre- and post-fire climatic deviations of each sampling site as the difference between site-specific climate conditions respect to the species niche centroid, and assessed their relationship with the success of post-fire regeneration. We found highly variable patterns of post-fire regeneration density of P.halepensis over the studied sites, ranging from 7 to 42,822 tree pines ha^-1. Generalized linear models indicated a positive relationship between fire severity and the density of P.halepensis regeneration. Positive temperature deviations - warm conditions - before fire were positively related to pine regeneration. This effect increases under higher fire severity. By contrast, warm temperatures after fire showed a negative effect on the density of pine trees. Positive precipitation deviations - wet conditions - after fire enhanced pine regeneration, while precipitation before fire did not had any significant effect. Though P.halepensis is considered a species adapted to fire and drought, the interaction between these two disturbances can alter the success of its post-fire recovery patterns limiting the species' resilience in the future.

Detecting critical nodes in forest landscape networks to reduce wildfire spread

Although wildfires are an important ecological process in forested regions worldwide, they can cause significant economic damage and frequently create widespread health impacts. We propose a network optimization approach to plan wildfire fuel treatments that minimize the risk of fire spread in forested landscapes under an upper bound for total treated area. We used simulation modeling to estimate the probability of fire spread between pairs of forest sites and formulated a modified Critical Node Detection (CND) model that uses these estimated probabilities to find a pattern of fuel reduction treatments that minimizes the likely spread of fires across a landscape. We also present a problem formulation that includes control of the size and spatial contiguity of fuel treatments. We demonstrate the approach with a case study in Kootenay National Park, British Columbia, Canada, where we investigated prescribed burn options for reducing the risk of wildfire spread in the park area. Our results provide new insights into cost-effective planning to mitigate wildfire risk in forest landscapes. The approach should be applicable to other ecosystems with frequent wildfires.

Utilizing the Available Open-Source Remotely Sensed Data in Assessing the Wildfire Ignition and Spread Capacities of Vegetated Surfaces in Romania

We bring a practical and comprehensive GIS-based framework to utilize freely available remotely sensed datasets to assess wildfire ignition probability and spreading capacities of vegetated landscapes. The study area consists of the country-level scale of the Romanian territory, characterized by a diversity of vegetated landscapes threatened by climate change. We utilize the Wildfire Ignition Probability/Wildfire Spreading Capacity Index (WIPI/WSCI). WIPI/WSCI models rely on a multi-criteria data mining procedure assessing the study area’s social, environmental, geophysical, and fuel properties based on open access remotely sensed data. We utilized the Receiver Operating Characteristic (ROC) analysis to weigh each indexing criterion’s impact factor and assess the model’s overall sensitivity. Introducing ROC analysis at an earlier stage of the workflow elevated the final Area Under the Curve (AUC) of WIPI from 0.705 to 0.778 and WSCI from 0.586 to 0.802. The modeling results enable discussion on the vulnerability of protected areas and the exposure of man-made structures to wildfire risk. Our study shows that within the wildland–urban interface of Bucharest’s metropolitan area, there is a remarkable building stock of healthcare, residential and educational functions, which are significantly exposed and vulnerable to wildfire spreading risk.

Prediction of regional wildfire activity in the probabilistic Bayesian framework of Firelihood

Modeling wildfire activity is crucial for informing science-based risk management and understanding the spatiotemporal dynamics of fire-prone ecosystems worldwide. Models help disentangle the relative influences of different factors, understand wildfire predictability, and provide insights into specific events. Here, we develop Firelihood, a two-component, Bayesian, hierarchically structured, probabilistic model of daily fire activity, which is modeled as the outcome of a marked point process: individual fires are the points (occurrence component), and fire sizes are the marks (size component). The space-time Poisson model for occurrence is adjusted to gridded fire counts using the integrated nested Laplace approximation (INLA) combined with the stochastic partial differential equation (SPDE) approach. The size model is based on piecewise-estimated Pareto and generalized Pareto distributions, adjusted with INLA. The Fire Weather Index (FWI) and forest area are the main explanatory variables. Temporal and spatial residuals are included to improve the consistency of the relationship between weather and fire occurrence. The posterior distribution of the Bayesian model provided 1,000 replications of fire activity that were compared with observations at various temporal and spatial scales in Mediterranean France. The number of fires larger than 1 ha across the region was coarsely reproduced at the daily scale, and was more accurately predicted on a weekly basis or longer. The regional weekly total number of larger fires (10-100 ha) was predicted as well, but the accuracy degraded with size, as the model uncertainty increased with event rareness. Local predictions of fire numbers or burned areas also required a longer aggregation period to maintain model accuracy. The estimation of fires larger than 1 ha was also consistent with observations during the extreme fire season of the 2003 unprecedented heat wave, but the model systematically underrepresented large fires and burned areas, which suggests that the FWI does not consistently rate the actual danger of large fire occurrence during heat waves. Firelihood enabled a novel analysis of the stochasticity underlying fire hazard, and offers a variety of applications, including fire hazard predictions for management and projections in the context of climate change.

A Practical Method for High-Resolution Burned Area Monitoring Using Sentinel-2 and VIIRS

Mapping burned areas using satellite imagery has become a subject of extensive research over the past decades. The availability of high-resolution satellite data allows burned area maps to be produced with great detail. However, their increasing spatial resolution is usually not matched by a similar increase in the temporal domain. Moreover, high-resolution data can be a computational challenge. Existing methods usually require downloading and processing massive volumes of data in order to produce the resulting maps. In this work we propose a method to make this procedure fast and yet accurate by leveraging the use of a coarse resolution burned area product, the computation capabilities of Google Earth Engine to pre-process and download Sentinel-2 10-m resolution data, and a deep learning model trained to map the multispectral satellite data into the burned area maps. For a 1500 ha fire our method can generate a 10-m resolution map in about 5 min, using a computer with an 8-core processor and 8 GB of RAM. An analysis of six important case studies located in Portugal, southern France and Greece shows the detailed computation time for each process and how the resulting maps compare to the input satellite data as well as to independent reference maps produced by Copernicus Emergency Management System. We also analyze the feature importance of each input band to the final burned area map, giving further insight about the differences among these events.

Association between Prevailing Circulation Patterns and Coarse Particles in Portugal

Air pollution is one of the major environmental concerns today, with high socio-economic and public health impacts, which are expected to worsen in the future due to anthropogenic warming. Among the toxic agents present in the atmosphere, coarse particles (PM10 and PM2.5) are some of the most harmful for human health and the environment. Therefore, the study of these particles and their association with meteorological constraining factors is of utmost importance. The aim of this study is to analyze the circulation weather types (CWT) affecting PM10 and PM2.5 concentrations measured at background monitoring stations in Portugal between 2006 and 2018. PM10 and PM2.5 were analyzed in terms of their intra-annual and inter-annual variability, their relations with CWT and the characteristics of two major extreme events over Portugal. The analysis of the extreme events relied on both background stations and through the near-real time data from Copernicus Atmosphere Monitoring Service (CAMS) outputs. The inter-annual assessment showed a decrease in concentrations over the studied years, mainly for PM10. Intra-annual assessment pointed to higher concentrations during winter months. Higher PM concentrations were mostly associated with CWTs with easterly or southerly components, characterized by low frequency of rainfall and advection of dry air masses. The two analyzed extreme events, associated with mega wildfires (15–17 October 2017) and dust intrusion (1–10 August 2018) were analyzed in more detail. Prominent southerly and easterly circulations were observed during the onset and peak of the events, which then later decayed due to the change to maritime flows (westerly and northerly circulation types) which dispersed the particles.